data mining weka book

Brief introduction In data mining with WEKA, part 1th: Introduction and regression, I introduced the concept of data mining and free open source software Waikato Environment for Knowledge Analysis (WEKA), which can be used to min

Today introduces a book, "Data Mining R language combat." Data mining technology is the most critical technology in the era of big data, its application fields and prospects are immeasurable. R is a very good statistical analysis

process statistics, analyze and visualize data. Through various examples, the reader can learn the core algorithm of machine learning, and can apply it to some strategic tasks, such as classification, prediction, recommendation. In addition, they can be used to implement some of the more advanced features, such as summarization and simplification. I've seen a part of this book before, but the internship in

1. The data analysis (Douban) book is quite simple. The basic content is involved, and it is clear. Finally, we talked about R as a plus.Difficulty level: very easy.2. Beer and diapers (Douban) are the most typical cases.Difficulty level: very easy.3. The beauty of data (Douban) An introductory book, each chapter solve

(written in front) said yesterday to write a machine learning book, then write one today. This book is mainly used for beginners, very basic, suitable for sophomore, junior to see the children, of course, if you are a senior or a senior senior not seen machine learning is also applicable. Whether it's studying intelligence or doing other things, machine learning is a must. You see GFW all use machine study,

Trust me, you'll like him. This is a book for learning basic data mining knowledge. Most of the books on data mining focus on theoretical knowledge, which is difficult to understand and daunting. Don't get me wrong, these theoretical knowledge is still very important. But

model parameters are adjusted to the optimal values. Generally, some techniques can solve the same kind of data mining problem. Some techniques have special requirements for data formation, so it is often necessary to jump back to the data preparation phase.Evaluation (Evaluation)At this stage, you have developed a hi

The idea of self-taught machine learning is really because of my interest in data mining, because in my heart I have always believed in the logic that there is a certain pattern behind everything, and that different situations only correspond to certain conditions. So to find such a pattern is the most convenient and quickest way to solve a class of problems, as a lazy person like me, of course, I would lik

spatial attributes, such as location or region. There are many examples of spatial data, such as collecting meteorological data from different places. An important feature of spatial data is space.Spatial autocorrelation)That is, the physically close objects tend to be similar to other aspects.Process non-recorded dataMost d